Learning algorithms for self organizing mapping based on partial distortion search
基于部分失真搜索的自组织映射学习算法

To accelerate the learning process of Self-Organizing Mapping in the situation of large mount of data or high dimension, two learning algorithms were proposed in this paper, by using Partial Distortion Search and Extended Partial Distortion Search respectively to solve the problem of Nearest Neighbor Search during learning process, which could reduce the multiplications greatly. Experiment results indicate that the proposed algorithms can save up to 1/3 and 1/2 multiplications, compared with traditional Self-Organizing Mapping learning algorithm.